Impact of risk and information coupled propagation in multilayer networks on supply chain resilience

With increased globalization, multiple countries are involved in supply chains, forming complex supply networks. Frequent occurrences of natural disasters, geopolitical instability, and global health crises pose unprecedented challenges to traditional supply chain management methods. Local disruptions in the supply chain can spread internally, causing a series of chain reactions. Enhancing supply chain risk resilience and robustness has become a research focus for many scholars. The widespread use of the Internet has led to rapid information exchange between enterprises; an increasing number of scholars have recognized the importance of early warning information in preventing supply chain disruptions. Therefore, understanding how information affects the propagation of risks within the supply chain and maximizing the early warning function of information have significant practical implications. Moreover, the heterogeneity in the responses of enterprises to early warning information also needs attention.

To capture the propagation of early warning information and disruption risks, a two-layer propagation model that couples risk and information is constructed. In this model, the upper layer represents the information layer and the lower layer represents the risk layer. The information of a disruption in a lower-layer enterprise is transmitted to upstream and downstream enterprises with a certain probability. After receiving the early warning information, an enterprise transitions into a conscious node and this transition is reflected in the upper layer network. In this model, there are five possible states for the nodes in the network. A microscopic Markov chain (MMC) method is used to analyze the state transition process between nodes and calculate the risk propagation threshold of the system. Furthermore, the key factors influencing the propagation of disruption risk are analyzed. An agent-based approach is used for case simulation to validate the model's effectiveness. Numerical analysis of the model reveals that the network structure, network size, extent of risk information propagation in the information layer, and the probability of disruption risk propagation are the key factors influencing the propagation of the risk. Financial data from Tesla's supply chain in China are also collected. In case simulation, an agent-based method is used to study the effects of the information layer network structure, information propagation rate, and risk propagation rate on the supply chain resilience.

The results show that for a low information propagation rate, the scale-free network structure accelerates information dissemination, allowing more enterprises to quickly obtain early warning information, thereby helping the supply chain resist risks and improve resilience. When the information propagation rate exceeds 0.4, the small-world network structures can propagate risks more efficiently because of their shorter average paths. Additionally, three disruption schemes are used to analyze system resilience, revealing that prioritizing the disruption of nodes with higher degrees has the greatest impact on the network, while deliberately attacking nodes with smaller degrees allows the supply chain to maintain higher operational efficiency. This finding suggests that maintaining the robustness of the key nodes in the supply chain is critical for enhancing the overall network resilience.

Adjusting the supply chain network structure can help improve the risk resilience and robustness of the system. Enhancing risk awareness of enterprises and their response strategies can effectively improve supply chain resilience and suppress risk diffusion. Deliberate attacks on hub nodes with high degrees cause the greatest damage to the network system. Thus, this study provides theoretical support for supply chain management and can serve as a basis for decision-making to improve supply chain risk resilience and optimize management strategies.